Wavelet-enhanced automated fingerprint identification system

ABSTRACT

A method and system for performing automated biological identification. The system including a preprocessing module with a histogram transform for locally and globally enhancing biological data such as fingerprints. An enhancement module with a fast smoothing and enhancement function. A feature extraction module with a fingerprint oriented thinning function. A matching module with a resolution-enhanced Hough transform function for fingerprint registration and matching score function.

This application is a Divisional of application Ser. No. 09/742,405,filed on Dec. 22, 2000 now U.S. Pat. No. 6,901,155, and for whichpriority is claimed under 35 U.S.C. § 120; and this application claimspriority under 35 U.S.C. § 119(e) on U.S. Provisional Application No(s).60/171,582 filed on Dec. 23, 1999, the entire contents of which arehereby incorporated by reference.

BACKGROUND OF THE INVENTION

1 Field of the Invention

The present invention relates to an improved method for characterizing,matching, and identifying biologically unique features such asfingerprints and irises. More specifically, it relates to methods forenhancement of digital images, fast directional convolution andfingerprint-oriented ridge thinning, matching and identification offingerprints.

2. Description of Related Art

As our society is increasingly electronically-connected, automatedpersonal authentication becomes more important than ever. Traditionaltechniques, such as those using personal identification numbers (PIN) orpasswords, will not satisfy demanding security requirements as they areincapable of differentiating between an authorized person and animpostor. In fact, these techniques can only verify the correctness ofthe PIN input by a person, but not authenticate the true identity of theauthorized person.

To overcome this shortcoming in personal authentication, a number ofbiometric techniques have been investigated. Biometric authenticationcapitalizes on some unique bodily features or characteristics of aperson, such as fingerprint, voice, hand geometry, face, palm, and irispattern. Among these biometric features, automated fingerprintidentification system (AFIS) has provided the most popular andsuccessful solution, mainly due to the uniqueness of fingerprints andthe historical legal aspect of fingerprinting for law enforcement.

A robust and efficient AFIS however, comes with many challenges. TheAFIS must be able to differentiate two different fingerprints that maybe seemingly identical to the untrained eye. The uniqueness of afingerprint is characterized by the finely embedded details (calledminutiae) of the print, and its overall ridge pattern and density. Froma legal standpoint, under Singapore's criminal laws, two fingerprintsthat contain 16 or more reliably matching minutiae are registered asoriginating from the same finger of the same person. As a result, asuccessful AFIS must have strong discrimination power, robustness tocertain degrees of deformation in the fingerprint, and fast (or evenreal-time) processing performance.

Typically, AFIS includes features such as fingerprint imagepre-enhancement, orientation filtering, ridge thinning, fingerprintregistration and weighted matching score computation. The need forfingerprint image pre-enhancement arises because regardless of theacquisition method and, device (either from fingerprint cards, or fromfingerprint readers such as optical sensors, or more recently,semiconductor sensors) fingerprints are susceptible to various forms ofdistortion and noise, including blotches caused by the inputenvironment, skin disease (cuts, and peeling skin), and skin condition(either too wet among younger people, or too dry among elder people). Asa result, fingerprint image enhancement is needed to suppress noise,improve contrast, and accentuate the predominant orientation informationof the fingerprint.

Orientational filters are generally used for image enhancement accordingto the local directions of fingerprint ridges, which are obtained fromthe orientation field of the fingerprint image. Prior art forpre-enhancing includes finding an accurate estimation of the orientationfield using some advanced but complicated models and employing a globalenhancement technique (e.g., M. Kass et al., “Analyzing OrientedPatterns”, Comput. Vis. Graphics Image Process, 37, 362–385, 1987. N.Ratha et al., “Adaptive Flow Orientation Based Feature Extraction inFingerprint Images”, Pattern Recognition, 28 (11), 1657–1672, 1995.Vizcaya et al., “A Nonlinear Orientation Model for Global Description ofFingerprints”, Pattern Recognition, 29 (7), 1221–1231, 1996.).Nevertheless,: these techniques are usually computationally expensive,and hence less suitable for most AFIS solutions that require, real-time,processing. Another class of pre-enhancement techniques firstaccentuates the orientation information and then estimates theorientation field. The most famous technique being the NIST's FFT-basedmethod (e.g., G. T. Candela, et al., “PCASYS-A Pattern-LevelClassification Automation System For Fingerprints”, National Instituteof Standards and Technology, Visual Image Processing Group, August1995.), and also some other variants of the FFT-based method (e.g.,Sherlock et al., “Fingerprint Enhancement by Directional FourierFiltering, Proc.” IEE Visual Image Signal Processing vol. 141 (2),87–94, April 1994).

After the pre-enhancement, orientation filtering is also commonly usedto further enhance an input fingerprint image. Many filters have beendesigned for fingerprint image enhancement (e.g. Gorman et al., “AnApproach To Fingerprint Filter Design”, Pattern Recognition, Vol. 22(1), 29–38, 1989; B. M. Mehtre, Fingerprint image analysis for automaticidentification, Machine Vision and Applications, 6, 124–139, 1993; Kameiet al., “Image Filter Design For Fingerprint Enhancement”, Proc.International Symposium on Computer Vision, 109–114, 1995; and Maio etal., “Direct Gray-Scale Minutiae Detection In Fingerprints”, IEEETransactions on PAMI, Vol. 19, No. 1, 27–40, January 1997), adopted amethod to filter the image using a class of orientation filters, andthen derive the fingerprint minutiae from the gray-scale image directly.Such a method required intensive computations (e.g., Kasaei et al.,“Fingerprint Feature Enhancement Using Block-Direction On ReconstructedImages”, TENCON '97. IEEE Region 10 Annual Conference, Speech and ImageTechnologies for Computing and Telecommunications, Proceedings of IEEE,vol. 1, 303–306, 1997 attempted to avoid the use of a large class offilters.) To do so, the original image is first rotated to a particulardirection to perform the orientation filtering, and then rotated back tothe original direction. This rotation process introduces loss ofaccuracy due to the quantization noise of rotating a discrete image,which may subsequently result in the detection of false minutiae.

Regarding ridge thinning, the prior art has consistently shown that themost effective and robust approach for fingerprint feature extraction isprobably using binarization. With this approach, the fingerprint ridgesare thinned into binary lines of width of only one pixel before theminutiae are extracted. Some prior art avoids binarization by performingthe feature extraction process directly on the grayscale image (e.g.,Maio et al., “Direct Gray-Scale Minutiae Detection In Fingerprints”,IEEE Transactions on PAMI, Vol. 19, No. 1, 27–40, January 1997). Such anapproach, however, has the drawbacks of missing minutiae and inaccurateminutiae position and direction. Further, many powerful thinningalgorithms have been developed for Chinese character recognitions butthey are generally not applicable for thinning ridges in fingerprintimages (e.g. Chen et al., “A Modified Fast Parallel Algorithm ForThinning Digital Patterns”, Pattern Recognition Letters, 7, 99–106,1988; R. W. Zhou, “A Novel Single-Pass Thinning Algorithm And AnEffective Set Of Performance Criteria”, Pattern Recognition Letters, 16,1267–1275, 1995; and Zhang, “Redundancy Of Parallel Thinning”, PatternRecognition Letters, Vol. 18, 27–35, 1997).

The conventional art includes many methods of fingerprint registration.Among them, minutia-based methods are the most popular approaches (e.g.Ratha et al., “A Real-Time Matching System For Large FingerprintDatabases”, IEEE Trans. PAMI, 18 (8), 799–813, August, 1996). Suchmethods make use of the positional and orientational information of eachminutia (e.g. Ratha et al., “A Real-Time Matching System For LargeFingerprint Databases”, IEEE Trans. PAMI, 18 (8), 799–813, August, 1996;Hrechak et al., “Automated Fingerprint Recognition Using StructuralMatching”, Pattern Recognition, 23(8), 893–904, 1990; Wahab et al.,“Novel Approach To Automated Fingerprint Recognition”, Proc. IEE VisualImage Signal Processing, 145(3), 160–166, 1998; and Chang et al., “FastAlgorithm For Point Pattern Matching: Invariant To Translations,Rotations And Scale Changes”, Pattern Recognition, 30(2) 311–320, 1997),or possibly together with a segment of ridge associated with the minutia(e.g. Jain et al., “An Identity-Authentication System UsingFingerprint”, Proc. IEEE, 85(9), 1365–1388, 1997). Some minutia-basedmethods implement registration based on only a few minutiae. Thesemethods are usually simple and fast in computation. However, since thesemethods depend mainly on the local information of a fingerprint, theycannot handle well the influence of fingerprint deformation and mayprovide an unsatisfied registration.

To overcome this problem, some other methods that exploit the globalfeatures of the prints have been developed. A typical example of suchmethods is to use the generalized Hough transform (Ratha et al., “AReal-Time Matching System For Large Fingerprint Databases”, IEEE Trans.PAMI, 18 (8), 799–813, August, 1996) to perform the registration. Thisapproach allows consideration of the contribution of all the detectedminutiae in the prints, and is efficient in computation.

In the weighted matching score computation, the matching score is thefinal numerical figure that determines if the input print belongs to anauthorized person by comparing the score against a predeterminedsecurity threshold value. Conventionally, the most used formula formatching score computation is given by the ratio of the number ofmatched minutiae to the product of the numbers of the input and templateminutiae. For example, suppose that D minutiae are found to be matchingfor prints P and Q. A matching score is then determined using theequation

${S = \sqrt{\frac{D^{2}}{MN}}},$where M and N are the number of the detected minutiae of P and Qrespectively. This way of computing the matching score is simple and hasbeen accepted as reasonably accurate.

However, such a matching score may be unreliable and inconsistent withrespect to a predetermined threshold. There are situations where twonon-matching prints can have a relatively high count of matchingminutia, as compared with a pair of matching prints. This results inrelatively close matching scores, which also means that thediscrimination power to separate between matching and non-matchingprints can be poor for a chosen security threshold value.

Therefore a demand exists to provide a method or a, system forcharacterizing, matching and identifying fingerprints or otherbiologically unique features, which improves on the above mentionedproblems of AFIS regarding image data pre-enhancement, orientationfiltering, ridge thinning, fingerprint registration and weightedmatching score computation.

SUMMARY OF THE INVENTION

It is the object of the present invention to provide a method or asystem for characterizing, matching and identifying fingerprints orother biologically unique features, which improves on the AFIS andincludes image data pre-enhancement, orientation filtering, ridgethinning, fingerprint registration and weighted matching scorecomputation.

These advantages are achieved by a biological data matching systemincluding an image reader, which acquires personal, biological data; atemplate of biological data; a pre-enhancing unit adapted to pre-enhancethe personal biological image data according to local areas of contrast;an image smoothing and enhancement filter for enhancing saidpre-enhanced image data; an orientation data thinner for removing falsedata in the personal biological image data; a registration unit foraligning the personal biological image data with the template image dataand a matching score unit for determining if the biological data matchesthe template print. Further, the personal biological data may be afingerprint, iris, voice, hand geometry, face or palm pattern, etc.

The advantages are further achieved by a finger print minutiaeextraction method including acquiring fingerprint image data;partitioning the fingerprint image data into at least one data blockcorresponding to a local area of the image; generating a histogramfunction of a contrast level of said image data corresponding to saiddata blocks; and performing a histogram transformation of the histogramfunction. Further, the histogram transformation is adapted to thecontrast level of the local area of the fingerprint image data andpre-enhanced fingerprint image data is generated with local enhancement.

Further scope of applicability of the present invention will becomeapparent from the detailed description given hereinafter. However, itshould be understood that the detailed description and specificexamples, while indicating preferred embodiments of the invention, aregiven by way of illustration only, since various changes andmodifications within the spirit and scope of the invention will becomeapparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will become more fully understood from thedetailed description given hereinbelow and the accompanying drawingswhich are given by way of illustration only, and thus are not limitativeof the present invention, and wherein:

FIG. 1 illustrates a flow chart of the AFIS according to the presentinvention;

FIG. 2 depicts an 8-neighborhood of pixel P;

FIG. 3 depicts a table of offset coordinates of directional convolutionfor filter with length 7 and 16 possible directions;

FIG. 4 depicts a lookup table (LUT₁) for fingerprint-oriented thinning;and

FIG. 5 depicts a lookup table (LUT₂) for fingerprint-oriented thinning.

DETAILED DESCRIPTION OF THE INVENTION

The invention is a method and system for performing AFIS with variousfeatures that contribute to a significant performance improvement to twoof the most important aspects of an AFIS: efficiency (fast processing),and reliability (accuracy and robustness to variations in inputfingerprints). In particular, these features include a histogrampre-enhancement method, fast smoothing and enhancement method forfingerprint images, a fingerprint-oriented thinning method, a modifiedHough transform for fingerprint registration, and an improved matchingscore computation method.

It is noted that while the specification discusses the invention inrelation to fingerprints, the invention is not limited to an applicationfor fingerprints and may also be used for other data image processingand matching such as iris, voice, hand geometry, face and palm patterns.

As illustrated in the FIG. 1, the system includes four modules: apreprocessing module 1, an enhancement module 2, a feature extractionmodule 3, and a matching module 4. The preprocessing module 1pre-enhances an input fingerprint image to remove some noise caused bythe fingerprint acquisition device or method 5 and removes the dominantridge directions. The enhancement module 2 further removes noise andaccentuates the desired features of the input fingerprint image so as toprovide a higher quality image for the other processing units. Thefeature extraction module 3 extracts all the fingerprint minutiae thatare unique and consistent features of an individual and provides thebasis for classification and identification. The matching module 4,implements fingerprint minutiae matching and fingerprint identificationand determines whether or not a fingerprint matches a templatefingerprint. The template fingerprint may be stored in a database 6.

The preprocessing module 1 includes a histogram transformer 10, dominantridge direction estimator 11 and coarse segmentation unit 12. Thehistogram transformer 10 receives finger print images from a fingerprint sensor/reader 5 or other data scanner or acquirer well known inthe art. The dominant ridge direction estimator 11 and coarsesegmentation device 12 receive the transformed fingerprint image data.The coarse segmentation 12 outputs the processed coarse segmentationimage data to the dominant ridge direction estimator 11, which performsthe estimation using the processed coarse segmentation image data andthe transformed fingerprint image data.

The enhancement module 2 includes a fine segmentation unit 20, anorientation smoothing and enhancing unit 21 and an enhanced fingerprintimage unit 22. The fine segmentation unit 20 and orientation smoothingand enhancing unit 21 receive the image data from the dominant ridgedirection estimator 11 and process the image data in parallel. Both thefine segmentation unit 20 and orientation smoothing and enhancing unit21 deliver processed data to the enhanced fingerprint image unit 22which uses the processed data to further enhance the image data.

The feature extraction module 3 includes a binalizing grayscale imageunit 31, a fingerprint oriented thinning unit 32, and a minutiaeextractor 33. The binalizing grayscale image unit 31 receives enhancedimage data from the enhancement module 2 and outputs binalized grayscaleimage data to the fingerprint oriented thinning unit 32. The fingerprintoriented thinning unit 32 outputs thinned image data to the minutiaeextractor 33 for processing.

The matching module includes a fingerprint registration unit 41 and amatching score computation unit 42. The fingerprint registration unit 41receives minutia data from the feature extraction module 3 and alignsthe current fingerprint against each of the template fingerprints in thedatabase 6. The matching score computation unit 42 provides a numericalfigure that represents the degree of matching between the currentfingerprint and a template fingerprint in the database 6. The AFISstores the template fingerprints and personal information of each personas a record in the database 6. During verification or identification,the unique features of a person's fingerprint are extracted, andsearched against the template fingerprints in the database 6.

The histogram transform 10 of the preprocessing module 1 performs thepre-enhancement histogram transformation of the finger print image datausing a simple but effective method including specially designedhistograms tailored to the statistical information of fingerprintimages. Unlike conventional histogram equalization methods that useconstant functions, a special function is used for global/localenhancement and adapts automatically to the histogram characteristics ofeach input fingerprint. The method can be implemented fast enough foron-line processing, and also gives better performance than approaches inexisting systems such as in Jain et al., “On-Line FingerprintVerification”, IEEE Trans. On Pattern Recognition and MachineIntelligence, 19 (4), 302–314, 1997.

The histogram transform 10 performs the following pre-enhancementfunctions. The fingerprint image is partitioned into image blocks ofsize S_(b)×S_(b). A block may be formed that encompasses the entirefingerprint image or several blocks may be formed with each blockencompassing only portions of the image. Histograms of pixel intensityon a pixel by pixel basis are generated for each block. Thecorresponding histogram function is also constructed for each block.Assuming that the histogram function for an image block is g(x), thehistogram transformer 10 maps histogram function of the image to aspecific function according to the following mapping

$ x\mapsto{\underset{y}{\arg\;\min}\{ {y❘{{\int_{0}^{x}{{g(t)}{\mathbb{d}t}}} < {f(y)}}} \}} $

where ƒ(x) is target histogram function. The target histogram functionhas low value at the mid-point and has a high value at the endpoint ofthe interval. An example of such function is

${{f(x)} = {N_{b} \times {( {x - \frac{M}{2}} )^{2}/{\int_{0}^{M}{( {x - \frac{M}{2}} )^{2}{\mathbb{d}x}}}}}},$

where M is the maximum value of possible intensity of pixel, such as 256for 8-bit per pixel fingerprint images, and N_(b) is the number ofpixels in the image/block, such as 256 for block of size 16×16. Ofcourse, the function ƒ(x) can be optimized to some other suitablefunctions of similar type.

Such a histogram transformation can be either global for the entireimage (by setting the block to be the entire image), or local for aportion of the image (using smaller blocks). When several blocks thatare smaller than the image are used to partition the fingerprint imagedata, a localization property of the transformation operator exists.That is the histograms, corresponding histogram functions andtransformations are generated for a sub-section or local area of thefingerprint image. Thus, the pre-enhancement can adapt to differentcontrast levels at different parts of the image and areas of differingcontrast levels may be processed more specifically. This subsequentlyallows better coarse segmentation of the image according to the mean andvariance values of each image block. Also, image blocks that containactual fingerprint ridges but are still blurred after the processing aremarked as background blocks, which are ignored so as to accelerate thepre-processing module.

The coarse segmentation 12 of preprocessing module 1 performs coarsesegmentation on every S_(b)×S_(b) block. The coarse segmentation unit 12identifies a block as a foreground block or a background block bycomparing the mean value and variance of the block with predeterminedthreshold and generates tags of every block. The tags have values 1 fora foreground block or 0 for a background block.

The dominant ridge direction estimator 11 in preprocessing module 1performs dominant ridge direction estimation according to an algorithmand using the tags generated by coarse segmentation unit 12 and thepre-enhanced image data generated by histogram transform 10. Only blockswith a tag value of 1 are processed in the dominant ridge directionestimator 11. The output of preprocessing module 1 is the index of oneof 16 discrete directions generated by dominant ridge directionestimator 12 and output image generated by histogram transform 10. Anexample of a dominant ridge estimation algorithm is that used in K.Jain, and H. Lin, “On-Line Fingerprint Verification, IEEE Trans. OnPattern Recognition And Machine Intelligence”, 19 (4), 302–314, 1997.Other suitable algorithms and processing methods may also be used.

Using the output of the pre-processing module 1, the fine segmentationunit of enhancement module 2 analyzes the smoothness of discretedirection of each foreground block and the corresponding 8-neighbourhoodblocks of the foreground block. The 8 neighbors of the foreground blockare arranged the as same as the pixels are depicted in FIG. 2. If thedifference of discrete direction of a foreground block between theaverage direction of 8-neighbor blocks is greater than a predeterminedthreshold, the foreground block, is segmented as a background block andthe tag of this block is assigned to 0.

The orientation smoothing and enhancing unit 21 of the enhancementmodule 2 performs orientation filtering using two convolution processes.A smoothing process and an enhancing process is imposed on everyforeground block image. First, the smoothing convolution for allforeground blocks occurs. Then, the foreground blocks are enhanced. Theconvolution is a directional convolution for a 2-dimensional digitalimage, and includes a convolution of the filter (low pass filter forsmoothing and high pass filter for enhancing, respectively) with thecurrent block image data by a directional filter. The convolution isimplemented by imposing on every pixel within the block the followingalgorithm:

${g( {i,j,k} )} = {\sum\limits_{l = 1}^{M}{{f( {{i + {y_{offset}(l)}},{j + {x_{offset}(l)}}} )} \times {h(l)}}}$

where g(i,j,k) is the output of image intensity at location, (i,j); k isindex of the dominant direction α_(k)=k×π/16 of current block forsmoothing processing and k is an index of the perpendicular direction ofdominant direction of current block for enhancing processing, and h(1)is the low pass filter with 7-tap for smoothing convolution and the highpass filter with 7-tap for enhancing convolution, respectively. Theoffset coordinates (x_(offset), y_(offset)) corresponding to discretedirection α_(k) are listed in FIG. 3.

The enhanced fingerprint image unit 22 sets all pixels of backgroundblock marked by fine segmentation unit 20 to 0 the output of orientationsmoothing and enhancing unit 21 and produces an enhanced fingerprintimage.

In the feature extraction module 3, the binalizinrg grayscale image unit31 and minutiae extractor 33 are standard algorithms used in imageprocessing (e.g. S. Kasaei, and M. Deriche, “Fingerprint FeatureEnhancement Using Block-Direction On Reconstructed Images”, TENCON '97.IEEE Region 10 Annual Conference. Speech and Image Technologies forComputing and Telecommunications, Proceedings of IEEE, vol. 1, 303–306,1997.

The input and output of binalizing grayscale image unit 31 are grayscaleimage and a black/white image, respectively. The input of minutiaeextractor 33 is a black/white image with one-pixel width curve/ridge andthe output of minutiae extractor 33 is a set of minutiae information inwhich there are attributes of one minutiae including the coordinate ofminutiae and the direction of this minutiae.

The fingerprint oriented thinning unit 32 of the feature extractionmodule 3 processes the output of the binalizing grayscale image unit 31giving special consideration to the unique properties of fingerprintridges and minutiae. Unlike character recognition applications, acritical problem in fingerprint recognition application is the formationof false connections that incorrectly link up two adjacent disjointridges. These false connections need to be removed, as they willsubsequently introduce false minutia and impair the accuracy of thematching module 4. The invention incorporates a set of rules thatredefine the behavior of the thinning algorithm such that there are muchfewer false connections after the thinning process.

The fingerprint oriented thinning unit 32 processes fingerprint data byusing an algorithm to apply LUT₁ and LUT₂, as shown in FIGS. 4 and 5,respectively, to each pixel foreground pixel P. An example of analgorithm that may be used is found in Chen et al., “A Modified FastParallel Algorithms For Thinning Digital Patterns”, Pattern RecognitionLetters, 7, 99–106, 1988. Other suitable algorithms may also be used.

The LUTs are formed using the following rules. Let A(P) be the number of0–1 (binary transition) patterns in the order set P₁, P₂, P₃, P₄, P₅,P₆, P₇, P₈, P₁, where P_(i), i=1, . . . , 8, are the 8-neighbors of theforeground pixel P in a clockwise direction (see FIG. 2), and B(P) isthe number of nonzero neighbors of P. The following rules are applied.

For 2≦B(P)≦7, we choose either

1. A(P)=1,

-   -   If P₁*P₃*P₅=0 and P₃*P₅*P₇=0 then LUT₁(P)=0;    -   If P₁*P₃*P₇=0 and P₁*P₅*P₇=0 then LUT₂(P)=0; or

2. A(P)=2,

-   -   If P₁*P₃=1 and P₅+P₆+P₇=0 then LUT₁(P)=0;    -   If P₃*P₅=1 and P₁+P₇+P₈=0 then LUT₁(P)=0;    -   If P₁*P₇=1 and P₃+P₄+P₅=0 then LUT₂(P)=0;    -   If P₅*P₇=1 and P₁+P₂+P₃=0 then LUT₂(P)=0.

The following new rules are incorporated into the new algorithm:

-   -   If P₁*P₇*P₈=1 and P₂+P₆>0 and P₃+P₅=0 then LUT₁(P)=0;    -   If P₅*P₆*P₇=1 and P₄+P₈>0 and P₁+P₃=0 then LUT₁(P)=0;    -   If P₁*P₂*P₃=1 and P₄+P₈>0 and P₅+P₇=0 then LUT₂(P)=0; and    -   If P₃*P₄*P₅=1 and P₂+P₆>0 and P₁+P₇=0 then LUT₂(P)=0.

The algorithms listed above in rule groups 1 and 2 can be found in Chenet al., “A Modified Fast Parallel Algorithm For Thinning DigitalPatterns”, Pattern Recognition Letters, 7, 99–106, 1988. Rule groups 1and 3 are designed for thinning of character images while the new rulesare designed for the thinning of biological data such as fingerprints.The set of new rules not only reduces the number of false connections,but also significantly cuts down the number of computations becauseduring each iteration, the fingerprint oriented thinning unit 32 iscapable of removing more pixels than other conventional methods.

The fingerprint oriented thinning unit 32 performs the followingiteration on each foreground pixel P. If the result of the lookup table,LUT₁, as shown in FIG. 4 is equal to zero, then the pixel P is removedby classifying it as background. The same procedure is then repeatedusing another lookup table, LUT₂, as shown in FIG. 5. This process isiterated on all foreground pixels until no pixels can be removed.

The fingerprint registration unit 41 in the matching module 4 hassignificant affect on the performance of the entire AFIS system. Duringregistration, the current fingerprint is aligned against each templatefingerprint in the database 6. The database 6 may contain one or morethan one templates. The invention performs fingerprint imageregistration using a resolution-enhanced generalized Hough transform.

The generalized Hough transform is defined as, follows (Rathal et al.,“A Real-Time Matching System For Large Fingerprint Databases”, IEEETrans. PAMI, 18 (8), 799–813, August, 1996): Let P and Q denote theminutia data sets extracted from an input, fingerprint image and atemplate, respectively. Usually, P and Q can be organized as P={(p_(x)^(l), p_(y) ^(l), α^(l)), . . . , (p_(x) ^(M), p_(y) ^(M), α^(M))}, andQ={(q_(x) ^(l), q_(y) ^(l), β^(l)), . . . , (q_(x) ^(N), q_(y) ^(N),β^(N))} , where (p_(x) ^(i), p_(y) ^(i), α^(i)) and (q_(x) ^(j), q_(y)^(j), β^(j)) are the features, i.e., the position and orientationassociated with the ith minutia in P and the jth minutia in Q, M and Nare the number of the detected minutiae of P and Q, respectively. Thegeneralized Hough transform aligns P against Q by determining thetranslation parameters Δx in x axis and Δy in y axis, and the rotationparameter θ. It first discretizes the parameter space into a latticewith θε{θ₁, . . . , θ₁}, Δxε{Δx₁, . . . , Δx_(J)}, and Δyε{Δy₁, . . . ,Δy_(K)}. Each combination {Δx_(i), Δy_(j), θ_(k)} is called a latticebin. Then, for each minutia in P and every minutia in Q, it computes thetransformation parameters {Δx, Δy, θ} byθ=β^(j)−α^(i)   (1)and

$\begin{matrix}{\begin{pmatrix}{\Delta\; x} \\{\Delta\; y}\end{pmatrix} = {\begin{pmatrix}q_{x}^{j} \\q_{y}^{j}\end{pmatrix} - {\begin{pmatrix}{\cos\;\theta} & {{- \sin}\;\theta} \\{\sin\;\theta} & {\cos\;\theta}\end{pmatrix}\begin{pmatrix}p_{x}^{i} \\p_{y}^{i}\end{pmatrix}}}} & (2)\end{matrix}$

and quantizes them into the above mentioned lattice. Each set of thequantized parameters obtained is said to be one ‘evidence’ of thecorresponding lattice bin. The generalized Hough transform counts thenumber of evidences for each bin in the lattice and finally specifiesthe parameters of the lattice bin that corresponds to the maximum numberof evidences as the parameters for the (actual) registration.

While the generalized Hough transform is efficient; in computation andworks well in many cases, even on partial information of prints, itsuffers from an inherent problem that limits its performance. To makethe registration accurate, it is desirable to use a relatively smallsize of lattice bins. This, however, will result in a low maximumevidence count, which means that the alignment will be less reliable. Onthe contrary, increasing the lattice bin size will lead to poor spatialresolution and thereby, low registration accuracy.

In the inventive AFIS, the standard generalized Hough transform ismodified such that it can simultaneously overcome the above-mentionedproblem, and yet retain the major advantages of the Hough transform. Themodification is as follows. 1) A sufficiently large lattice bin size ischosen experimentally to ensure that enough evidences can be accumulatedwithin a bin. 2) The number of evidences for each of the bins of thelattice is counted as is done in the generalized Hough transform. 3) Thelattice is shifted in the x and y directions at a predetermined stepsize, and further, the number of evidences for each bin of the shiftedlattice is counted. The shifting and counting processes are repeated andstops until a bin at the final position of shifting is overlappedcompletely with its diagonal neighbor at the position before theshifting process. Note that shifting the lattice essentially partitionseach bin into blocks. In a direction, each block has the same size asthe shift step. 4) For each shift, each block is assigned a number equalto the counts of evidence of the bin in which the block is contained. 5)All such numbers of the overlapped blocks for each block position aresummed, and the transform parameters are specified as the positionparameters of the block that corresponds to the maximum sum.

Testing was performed for the various bin and shifting step sizes withas many prints as possible so as to determine the best translation androtation parameters. Currently, the bin size is Δx=16 pix, Δy=16 pix,and θ=10°. The step size of shifting is 4 pix in both the x and ydirections. These values however may be changed because they arerelevant to the fingerprint sensor used in a system.

The advantage of this approach, as compared with the ordinarygeneralized Hough transform, is that the registration accuracy andreliability now are determined by shift step and bin size, respectively,which overcomes the shortcomings mentioned previously. The accuracy ofregistration is increased by decreasing the shift step size whilesimultaneously maintaining the reliability. Although this approach addssome computations, application tests verified that it is suitable forreal-time applications.

The matching score computation unit 42 provides the final numericalfigure that determines if the input print belongs to an authorizedperson by comparing the score against a predetermined security thresholdvalue. To overcome the problems in the traditional art, the inventionincorporates a method of computing a matching score that is morereliable and has a much improved discrimination capability. This methodexploits the features of the above approach for fingerprint registrationusing shifted lattices. It was found that although the number of matchedminutia between two non-matching prints can be relatively high, thedistribution of the evidence counts over the lattices can be largelydifferent from that of two matching prints. For non-matching prints, themaximum value of evidence counts is low while the variance of theparameters of the lattice bins corresponding to the maximum evidencecounts is large (usually more than one bins have the maximum evidencecounts). In addition to using the number of matched minutia, the methodtakes into consideration these two factors in the computation of thefinal matching score.

Sigmoid nonlinear functions are used to weigh the contribution of eachof the three factors.

$\begin{matrix}{{S = {{\sigma( {w_{m}( {m - m_{0}} )} )}{\sigma( {w_{v}( \frac{1}{v - v_{0}} )} )}{\sigma( {w_{D}( {D - D_{0}} )} )}\sqrt{\frac{D^{2}}{MN}}}},} & (3)\end{matrix}$

where m is the maximum value of evidence counts, v is the variance ofthe parameters of the lattice bins corresponding to the maximum evidencecounts, D is the number of the matched minutiae, M and N are the numberof the detected minutiae in the current and template prints, and w_(m),w_(v), w_(D), m₀, v₀ and D₀ are prespecified parameters determined byexperiments, respectively.

${\sigma(\bullet)} = \frac{1}{1 + {\exp( {- (\bullet)} )}}$is the sigmoid function.

Experimental tests confirmed that this formulation provides much betterdiscrimination between matched and unmatched pairs than the traditionalcomputation of matching score. Also, this improvement could be achievedwith almost no extra computations.

The declared AFIS system can be used for both one-to-one matching aswell as for one-to-many matching against database prints of authorizedpersons. As an example of the matching speed, the correct identificationof an individual from a database of about 100 persons will take lessthan 1.5 seconds, while a one-to-one matching will take only about 0.4second (using a Pentium Pro 200 processor). The system is also veryrobust to variations in input fingerprints. For example, the system canstill correctly authenticate a person with an input fingerprint that isof low captured quality, and with some portions of the print removed.

Some applications of the invention include but are not limited to thefollowing: secured transactions (e.g., using credit card for Internetbanking and retailing, and for user authentication in automated tellermachines); secured access control (e.g., lift and door access inbuildings, and computer or network access without using passwords orcards); secured database systems (e.g., medical database of patients,nation-wide database for immigration and identification control;time-stamping database of workers in a company, and logistic databasefor controlling equipment checkout, or for monitoring movements ofprisoners).

The invention being thus described, it will be obvious that the same maybe varied in many ways. Such variations are not to be regarded as adeparture from the spirit and scope of the invention, and all suchmodifications as would be obvious to one skilled in the art are intendedto be included within the scope of the following claims.

1. A method for fingerprint registration and verification from minutiaecomprising: performing a Hough transform minutiae and generatingevidences in lattice bins; counting the evidences accumulated in saidlattice bin; shifting a lattice; determining the number of evidences ineach bin of said shifted lattice; repeating said shifting and countingin each direction of said lattice until a bin is completely overlappedwith its diagonal neighbor, wherein shifting the lattice enhances thespatial resolution of the Hough transform.
 2. The method of claim 1,wherein said shifting said lattice occurs at a predetermined step size.3. The method of claim 1, wherein said shifting the lattice partitionseach bin into blocks, each block is assigned a number equal to thenumber of evidences in the corresponding bin, the numbers of theoverlapped blocks are summed, and transform parameters are specifiedusing the block that corresponds to the highest sum.
 4. The method ofclaim 1, further comprising: determining the maximum number of evidencecounts in the bins; determining transformation parameters correspondingto the bins with the maximum evidence counts; determining the varianceof said transformational parameters; determining a matching score of afingerprint image and a template fingerprint image based on saidvariance of the transformational parameters and said maximum number ofcounts.
 5. The method of claim 4, wherein the matching score isdetermined using a sigmoid nonlinear function.
 6. The method accordingto claim 1, wherein the minutiae is finger print image data.
 7. Themethod according to claim 1, wherein the step of performing a Houghtransform on the minutiae and generating the evidences in the latticebins includes performing a modified resolution-enhanced generalizedHough transform.
 8. A method for fingerprint registration andverification from minutiae comprising: performing a resolution-enhancedgeneralized Hough transform on minutiae and generating evidences inlattice bins; counting the evidences accumulated in said lattice bin;shifting a lattice; determining the number of evidences in each bin ofsaid shifted lattice; repeating said shifting and counting in eachdirection of said lattice until a bin is completely overlapped with itsdiagonal neighbor, wherein shifting the lattice enhances the spatialresolution of the Hough transform, and wherein said shifting the latticepartitions each bin into blocks, each block is assigned a number equalto the number of evidences in the corresponding bin, the numbers of theoverlapped blocks are summed, and transform parameters are specifiedusing the block that corresponds to the highest sum.
 9. The method ofclaim 8, wherein said shifting said lattice occurs at a predeterminedstep size.
 10. The method of claim 8, further comprising: determiningthe maximum number of evidence counts in the bins; determiningtransformation parameters corresponding to the bins with the maximumevidence counts; determining the variance of said transformationalparameters; determining a matching score of a fingerprint image and atemplate fingerprint image based on said variance of thetransformational parameters and said maximum number of counts.
 11. Themethod of claim 8, wherein the matching score is determined using asigmoid nonlinear function.
 12. The method according to claim 8, whereinthe minutiae is finger print image data.
 13. A method for fingerprintregistration and verification from minutiae comprising: performing aresolution-enhanced generalized Hough transform on minutiae andgenerating evidences in lattice bins; counting the evidences accumulatedin said lattice bin; shifting a lattice; determining the number ofevidences in each bin of said shifted lattice; repeating said shiftingand counting in each direction of said lattice until a bin is completelyoverlapped with its diagonal neighbor, wherein shifting the latticeenhances the spatial resolution of the Hough transform, and furthercomprising the steps of: determining the maximum number of evidencecounts in the bins; determining transformation parameters correspondingto the bins with the maximum evidence counts; determining the varianceof said transformational parameters; and determining a matching score ofa fingerprint image and a template fingerprint image based on saidvariance of the transformational parameters and said maximum number ofcounts.
 14. The method of claim 13, wherein said shifting said latticeoccurs at a predetermined step size.
 15. The method of claim 13, whereinsaid shifting the lattice partitions each bin into blocks, each block isassigned a number equal to the number of evidences in the correspondingbin, the numbers of the overlapped blocks are summed, and transformparameters are specified using the block that corresponds to the highestsum.
 16. The method of claim 13, wherein the matching score isdetermined using a sigmoid nonlinear function.
 17. The method accordingto claim 13, wherein the minutiae is finger print image data.